mlsanityData to 14 Oct 2026

Credit Risk Analysis for SMEs Using Graph Neural Networks in Supply Chain

arXiv 2507.07854 ↗First posted 10 Jul 2025cs.LGcited by 2

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Abstract (arXiv)

Small and Medium-sized Enterprises (SMEs) are vital to the modern economy, yet their credit risk analysis often struggles with scarce data, especially for online lenders lacking direct credit records. This paper introduces a Graph Neural Network (GNN)-based framework, leveraging SME interactions from transaction and social data to map spatial dependencies and predict loan default risks. Tests on real-world datasets from Discover and Ant Credit (23.4M nodes for supply chain analysis, 8.6M for default prediction) show the GNN surpasses traditional and other GNN baselines, with AUCs of 0.995 and 0.701 for supply chain mining and default prediction, respectively. It also helps regulators model supply chain disruption impacts on banks, accurately forecasting loan defaults from material shortages, and offers Federal Reserve stress testers key data for CCAR risk buffers. This approach provides a scalable, effective tool for assessing SME credit risk.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 47 references that are in the map. First posted on arXiv 10 Jul 2025.

Builds on

18 of 47 references.

References · 18

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  10. 2025CTLformer: A Hybrid Denoising Model Combining Convolutional Layers and Self-Attention for Enhanced CT Image Reconstructioncited by 7
  11. 2025LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversioncited by 7
  12. 2025Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networkscited by 6
6 more
  1. 2025Credit Risk Identification in Supply Chains Using Generative Adversarial Networkscited by 5
  2. 2025Towards Multimodal Large-Language Models for Parent-Child Interaction: A Focus on Joint Attentioncited by 5
  3. 2025Regression and Forecasting of U.S. Stock Returns Based on LSTMcited by 4
  4. 2025A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planningcited by 4
  5. 2024Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithmscited by 3
  6. 2024An Optimal Control Approach for Inverse Problems with Deep Learnable Regularizerscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Temporal Visual Semantics-Induced Human Motion Understanding with Large Language Modelscited by 1
  2. 2026ScatterFusion: A Hierarchical Scattering Transform Framework for Enhanced Time Series Forecastingcited by 0